沪宁高速公路高温预警指标及预报模型的研究

利用沪宁高速公路实时监测数据,筛选出30个典型高温天气过程,应用WRF模式对其进行了数值模拟。在对输出结果作统计分析后,提取了沪宁高速公路高温天气的几个数值预警指标,建立了梅村、河阳两站的高温预报模型。研究表明:(1)利用WRF模式对沪宁高速公路沿线的高温天气过程进行模拟是可行的;(2)提取的沪宁高速公路高温数值预警指标有:前一日14时的地表温度Ts≥40℃、地面潜热通量Fl≥350 W.m-2、近地面相对湿度Hr≤60%、当日08时的地面感热通量Fs为负值且绝对值≥70 W.m-2、地面水平风速Vs≤3 m.s-1,当各项指标同时满足时,可预报当日会出现35℃以上的高温;(3)采用多元线性回归方法分别建立了梅村和河阳站的高温天气预报模型,经检验所建模型预报准确率较高。     

北京急性脑血管疾病与气象要素的关系及预测

基于2006年1月至2010年12月北京市120急救中心的逐日脑血管急症接诊病例数据资料,首先探讨北京市急性脑血管疾病与气象要素的关系,选取不同季节的影响因子,然后根据概率积分方法将发病人数划分为4个级别,并采用人工神经元网络方法(artificial neural network,ANN)分别建立了北京市不同季节的急性脑血管疾病预测模型。研究结果表明:(1)急性脑血管疾病发病人数存在明显的季节性变化和日变化特征,冬春季发病人数高于夏、秋季,发病主要集中在早晨到中午的09—14时;(2)发病人数相对于气象要素存在明显的滞后效应,夏和冬秋季发病分别与高温高湿、冷空气活动有关;(3)脑血管疾病预测模型通过对新样本进行预报,除夏季外,完全准确率高于30%,预报误差≤±1级的准确率高于60%,研究成果对于预防急性脑血管疾病发病和调度120急救车辆等应急措施具有较好的科学参考价值。     

Uncertainty analysis of statistical downscaling models using Hadley Centre Coupled Model

Regression-based statistical downscaling is a method broadly used to resolve the coarse spatial resolution of general circulation models. Nevertheless, the assessment of uncertainties linked with climatic variables is essential to climate impact studies. This study presents a procedure to characterize the uncertainty in regression-based statistical downscaling of daily precipitation and temperature over a highly vulnerable area (semiarid catchment) in the west of Iran, based on two downscaling models: a statistical downscaling model (SDSM) and an artificial neural network (ANN) model. Biases in mean, variance, and wet/dry spells are estimated for downscaled data using vigorous statistical tests for 30 years of observed and downscaled daily precipitation and temperature data taken from the National Center for Environmental Prediction reanalysis predictors for the years of 1961 to 1990. In the case of daily temperature, uncertainty is estimated by comparing monthly mean and variance of downscaled and observed daily data at a 95 % confidence level. In daily precipitation, downscaling uncertainties were evaluated from comparing monthly mean dry and wet spell lengths and their confidence intervals, cumulative frequency distributions of monthly mean of daily precipitation, and the distributions of monthly wet and dry days for observed and modeled daily precipitation. Results showed that uncertainty in downscaled precipitation is high, but simulation of daily temperature can reproduce extreme events accurately. Finally, this study shows that the SDSM is the most proficient model at reproducing various statistical characteristics of observed data at a 95 % confidence level, while the ANN model is the least capable in this respect. This study attempts to test uncertainties of regression-based statistical downscaling techniques in a semiarid area and therefore contributes to an improvement of the quality of predictions of climate change impact assessment in regions of this type.     

利用神经网络方法建立热带气旋强度预报模型

以神经网络方法为基础,建立西北太平洋热带气旋强度预测模型,模型首先进行历史相似热带气旋选择。从选择的样本出发,计算得到一组气候持续因子、天气学经验因子和动力学因子, 对这些因子采用逐步回归方法进行筛选,将筛选得到的因子同对应时效的热带气旋强度输入神经网络训练模块,从而得到优化的预测模型。从2004-2005年西北太平洋26个热带气旋过程对12,24,36,48,72h等不同预报时效分别进行的634,582,530,478,426次预测试验结果的统计来看,相对于线性回归模型预测水平,该模型显著降低了各时段的预测误差。从几个热带气旋个例的预测结果来看, 该模型对超强台风, 以及具有强度迅速加强、再次加强等特征的热带气旋过程均有很好的描述能力。     

利用RAMS模式对山谷城市冬季局地风场的数值模拟

利用美国科罗拉多州立大学和MRC/ASTER发展的中尺度数值模式RAMS, 采用三重嵌套的方法, 模拟研究了兰州山谷地区局地环流特征。结果表明： （1）兰州市近地面流场以偏东风为主, 在城市东西部之间的狭窄地带, 风速相对较大些, 在东西部山谷城市中心区域有大片的静风区; 冬季兰州市山谷夜间是辐合流场, 白天是辐散流场。受城市热岛环流的影响, 白天热岛环流抑制谷风环流, 夜间增大山风环流, 夜间的山风风速大于白天的谷风风速。（2）白天, 兰州市区山顶和山谷之间上空气柱以下沉气流为主, 这主要是由于地形作用使得白天盛行谷风环流和山峰加热作用的共同影响。夜间, 兰州市区山顶和山谷之间上空以上升气流为主, 这主要是由于地形作用使得市区和山谷在夜间盛行山风环流, 但是冬天夜间兰州市区和山谷上空有较厚的逆温层存在, 抑制了气流的上升运动。（3）在午后13：00左右, 兰州市区山谷从近地面到400 m高度, 辐散场在逐渐减弱, 在510 m左右的高度转变为辐合场; 皋兰山顶上空从近地面到400 m高度, 辐合场在逐渐减弱, 在510 m左右的高度转变为辐散场。在凌晨01：00左右, 兰州市区山谷从近地面到400 m高度, 辐合场在逐渐增强, 到400 m高度达到最强, 从400 m到510 m高度又逐渐减弱; 皋兰山顶上空从近地面到220 m左右的高度, 辐散场在逐渐减弱, 在400 m左右的高度辐散场转变为辐合场, 从400 m到510 m左右的高度, 皋兰山顶的辐合场逐渐增强。     

The effect of ozone photochemistry on atmospheric and surface temperature changes due to increased CO2, N2O,CH4 and volcanic aerosols in the atmosphere

Abstract

A coupled 1‐D radiative‐convective and photochemical diffusion model is used to study the influence of ozone photochemistry on changes in the vertical temperature structure and surface climate resulting from the doubling of atmospheric CO₂, N₂O, CH₄ and increased stratospheric aerosols owing to the El Chichón volcanic eruption. It is found when CO₂ alone is doubled, that the total ozone column increases by nearly 6% and the resulting increase in the solar heating contributes a smaller temperature decrease in the stratosphere (up to 4 K near the stratopause level). When the concentration of CO₂, N₂O and CH₄ are simultaneously doubled, the total ozone column amount increases by only 2.5% resulting in a reduced temperature recovery in the stratosphere. Additional results concerning the effect of the interaction of ozone photochemistry with the stratospheric aerosol cloud produced by the El Chichón eruption show that it leads to a reduction in stratospheric ozone, which in turn has the effect of increasing the cooling at the surface and above the cloud centre while causing a slight warming below in the lower stratosphere.     

Performance comparison of three predictor selection methods for statistical downscaling of daily precipitation

Predictor selection is a critical factor affecting the statistical downscaling of daily precipitation. This study provides a general comparison between uncertainties in downscaled results from three commonly used predictor selection methods (correlation analysis, partial correlation analysis, and stepwise regression analysis). Uncertainty is analyzed by comparing statistical indices, including the mean, variance, and the distribution of monthly mean daily precipitation, wet spell length, and the number of wet days. The downscaled results are produced by the artificial neural network (ANN) statistical downscaling model and 50 years (1961–2010) of observed daily precipitation together with reanalysis predictors. Although results show little difference between downscaling methods, stepwise regression analysis is generally the best method for selecting predictors for the ANN statistical downscaling model of daily precipitation, followed by partial correlation analysis and then correlation analysis.     

Retrieval of water vapor profiles with radio occultation measurements using an artificial neural network

A new method applying an artificial neural network （ANN） to retrieve water vapor profiles in the troposphere is presented. In this paper, a fully-connected, three-layer network based on the backpropagation algorithm is constructed. Month, latitude, altitude and bending angle are chosen as the input vectors and water vapor pressure as the output vector. There are 130 groups of occultation measurements from June to November 2002 in the dataset. Seventy pairs of bending angles and water vapor pressure profiles are used to train the ANN, and the sixty remaining pairs of profiles are applied to the validation of the retrieval. By comparing the retrieved profiles with the corresponding ones from the Information System and Data Center of the Challenging Mini-Satellite Payload for Geoscientific Research and Application （CHAMP-ISDC）, it can be concluded that the ANN is relatively convenient and accurate. Its results can be provided as the first guess for the iterative methods or the non-linear optimal estimation inverse method.     

基于ECMWF产品的站点气温预报集成学习误差订正

提出一种基于数值模式预报产品的气温预报集成学习误差订正方法,通过人工神经网络、长短期记忆网络和线性回归模型组合出新的集成学习模型（ALS模型）,采用2013—2017年的欧洲中期天气预报中心数值天气预报模式2 m气温预报产品和中国部分气象站点数据,利用气象站点气温、风速、气压、相对湿度4个观测要素,挖掘观测数据的时序特征并结合模式2 m气温预报结果训练机器学习模型,对2018年模式2 m气温6~168 h格点预报产品插值到站点后的预报结果进行偏差订正。结果表明:ALS模型可将站点气温预报整体均方根误差由3.11℃降至2.50℃,降幅达0.61℃（19.6%）,而传统的线性回归模型降幅为0.23℃（8.4%）。ALS模型对站点气温预报误差较大的区域和气温峰值预报的订正效果尤为显著,因此,集成学习方法在数值模式预报结果订正中具有较大的应用潜力。     

结合气象模式与GloBEIS模式研究气象条件对BVOCs排放的影响

BVOCs对全球碳收支、对流层化学反应及臭氧的形成和气候变化都有很大的影响.选取森林覆盖率高且BVOCs排放尚未有研究报道的中国东北地区作为研究区域,利用美国NCAR中心提供的1°×1°的6 h NCEP再分析资料,运行MM5模式,得到模拟结果,从中提取GloBEIS模型所需要的近地面层的温度、湿度、风速和云量的格点气象数据,实现了将MM5模式与GloBEIS相结合对BVOCs进行研究.研究中需要的植被种类及分布数据来自于最新的"植被信息系统"数据库,选取温度较高,太阳辐射较强,植被生长茂盛的2006年7月和2010年7月作为模拟时段,运行GloBEIS模型,对研究区域内的BVOCs的排放情况进行了估算.模拟结果与温度和云量等气象要素的分析结果表明,异戊二烯的排放速率受温度和PAR(光合有效辐射通量)的共同影响,日变化趋势显著,随着温度升高,PAR增强,异戊二烯的排放速率增大,在午后14:00左右达到最大值,之后降低.由于云量与PAR成反比,因此云量越少,异戊二烯的排放量就越高,反之,则越低;与异戊二烯不同,温度是单萜烯和其他VOC的排放的主导因素,受PAR和云量的影响较小,温度越高,排放量越大,反之,则越小.     

Estimating daily pan evaporation using artificial neural network in a semi-arid environment

The objective of this study was to test an artificial neural network (ANN) for estimating the evaporation from pan (E _Pan) as a function of air temperature data in the Safiabad Agricultural Research Center (SARC) located in Khuzestan plain in the southwest of Iran. The ANNs (multilayer perceptron type) were trained to estimate E _Pan as a function of the maximum and minimum air temperature and extraterrestrial radiation. The data used in the network training were obtained from a historical series (1996–2001) of daily climatic data collected in weather station of SARC. The empirical Hargreaves equation (HG) is also considered for the comparison. The HG equation calibrated for converting grass evapotranspiration to open water evaporation by applying the same data used for neural network training. Two historical series (2002–2003) were utilized to test the network and for comparison between the ANN and calibrated Hargreaves method. The results show that both empirical and neural network methods provided closer agreement with the measured values (R ²?>?0.88 and RMSE?<?1.2 mm day^?1), but the ANN method gave better estimates than the calibrated Hargreaves method.     

Studying the showers and thunderstorms on the territory of Russia using the numerical model of convective cloud and the reanalysis data

The computation of thunderstorm and shower activity on the territory of Russia during the warm period (June–August) of 1981–2000 for four observation times (00:00, 06:00, 12:00, and 18:00) is carried out using the local convective cloud model (CCM) and the ERA-40 reanalysis data on the vertical distribution of temperature and humidity. The spatial grid with the resolution of 2.5 × 2.5° is used for the computation. Collected and analyzed are the long-term (1936–1965) in situ data on the distribution of the number of days with the thunderstorm on the territory of Russia using the observational data from the ground-based meteorological stations (about 600 stations located in different regions). As a result, the distribution of the number of days with the thunderstorm and with the convective precipitation on the territory of Russia is plotted and analyzed. It agrees on the whole with the observed data. It is demonstrated that the number of days with the thunderstorm and with the convective precipitation correlate well with each other, that also corresponds to the observational data. It is shown that CCM is applicable to the simulation of cloud convection and associated phenomena.     

河西走廊东部冬季沙尘暴的典型个例及气候特征分析

应用1971—2016年河西走廊东部代表站的地面观测资料、NCEP 2.5°×2.5°月均地面至300 hPa高空资料,2006—2016年民勤逐日07和19时每隔10 m加密高空资料,分析了近45年河西走廊东部冬季沙尘暴天气的年际变化特征。同时选取2016年11月两次沙尘暴天气过程从天气学成因、物理量场及近地面边界层特征等方面进行了诊断分析。结果表明:近45年河西走廊东部冬季沙尘暴日数呈减少趋势,产生大风沙尘天气的主要原因不仅与大型冷暖空气强度及环流形势有关,还与冷锋过境时间、日变化、近地层风速和干湿程度关系密切。夜间至早晨近地面逆温厚且强,大气层结稳定,削弱沙暴强度,而午后到傍晚,逆温薄而弱,大气层结不稳定性强,加强了动量下传和风速,增强沙暴强度。近地层越干,风速越大,沙暴越强。     

Prediction of soil temperature using regression and artificial neural network models

In this study, monthly soil temperature was modeled by linear regression (LR), nonlinear regression (NLR) and artificial neural network (ANN) methods. The soil temperature and other meteorological parameters, which have been taken from Adana meteorological station, were observed between the years of 2000 and 2007 by the Turkish State Meteorological Service (TSMS). The soil temperatures were measured at depths of 5, 10, 20, 50 and 100 cm below the ground level. A three-layer feed-forward ANN structure was constructed and a back-propagation algorithm was used for the training of ANNs. In order to get a successful simulation, the correlation coefficients between all of the meteorological variables (soil temperature, atmospheric temperature, atmospheric pressure, relative humidity, wind speed, rainfall, global solar radiation and sunshine duration) were calculated taking them two by two. First, all independent variables were split into two time periods such as cold and warm seasons. They were added to the enter regression model. Then, the method of stepwise multiple regression was applied for the selection of the “best” regression equation (model). Thus, the best independent variables were selected for the LR and NLR models and they were also used in the input layer of the ANN method. Results of these methods were compared to each other. Finally, the ANN method was found to provide better performance than the LR and NLR methods.     

Stratospheric response to chemical perturbations

The paper presents a coupled chemical-radiative one-dimensional model which is used to assess the steady-state and time-dependent composition and temperature changes in relation to the release in the atmosphere of chemicals such as CO₂, N₂O, CH₄, NO _x and chlorofluorocarbons.The model indicates that a doubling in CO₂ leads to an increase in temperature of 12.7 K near the stratopause and to an increase in total ozone of 3.3% with a local enhancement of 17% at 40 km altitude. Additional release of N₂O leads to an ozone reduction in the middle stratosphere. The reduction in the ozone column is predicted to be equal to 8.8% when the amount of N₂O is doubled. The chemical effect of CH₄ on ozone is particularly important in the troposphere. A doubling in the mixing ratio of this gas enhances the O₃ concentration by 11% at 5 km. The predicted increase of the ozone column is equal to 1.4%. A constant emission of CFCl₃ (230 kT/yr) and CF₂Cl₂ (300 kT/yr) leads to a steady-state reduction in the ozone column of 1.9% compared to the present-day situation. The effect of some uncertainties in the chemical scheme as well as the impact of a high chlorine perturbation are briefly discussed.Finally the results of a time dependent calculation assuming a realistic scenario for the emission of chemical species are presented and analyzed.     

全国棉花发育期业务预报方法研究

棉花发育期是反映棉花个体与群体生育状况的重要标志, 正确预报棉花发育期对于开展棉花气象灾害预警防御和生产管理措施建议至关重要。为此, 针对全国农业气象业务服务中尚未开展棉花发育期预报的状况, 通过对全国农业气象观测网50个站棉花发育期的变异性和影响棉花生长发育速率因子的分析, 根据实际业务资料现状和业务运行特点, 结合天气预报, 提出了以棉花发育阶段有效积温和间隔日数为指标建立棉花发育期业务预报模式的思路, 实现了可实时动态预报棉花发育期的业务运行方法。模式历史拟合、外推和试预报与实际对比的结果表明:全国50个站棉花第五真叶和开花发育期误差在5 d以内出现的频次达80%以上, 出苗、第三真叶、现蕾等发育期在70%以上, 平均绝对误差小于4 d; 裂铃期误差在6 d以内出现的频次接近70%, 平均绝对误差小于6 d, 效果较好。对棉花停止生长发育期综合考虑积温和初霜日期两个因素, 其历史拟合、外推和试预报误差在10 d以内出现的频次接近70%, 平均绝对误差接近10 d, 基本可以满足全国农业气象业务以旬为服务时效的要求。     

Sounding-derived indices for neural network based short-term thunderstorm and rainfall forecasts

A neural network-based scheme to do a multivariate analysis for forecasting the occurrence and intensity of a meteo event is presented. Many sounding-derived indices are combined together to build a short-term forecast of thunderstorm and rainfall events, in the plain of the Friuli Venezia Giulia region (hereafter FVG, NE Italy).For thunderstorm forecasting, sounding, lightning strikes and mesonet station data (rain and wind) from April to November of the years 1995–2002 have been used to train and validate the artificial neural network (hereafter ANN), while the 2003 and 2004 data have been used as an independent test sample. Two kind of ANNs have been developed: the first is a “classification model” ANN and is built for forecasting the thunderstorm occurrence. If this first ANN predicts convective activity, then a second ANN, built as a “regression model”, is used for forecasting the thunderstorm intensity, as defined in a previous article.The classification performances are evaluated with the ROC diagram and some indices derived from the Table of Contingency (like KSS, FAR, Odds Ratio). The regression performances are evaluated using the Mean Square Error and the linear cross correlation coefficient R.A similar approach is applied to the problem of 6 h rainfall forecast in the Friuli Venezia Giulia plain, but in this second case the data cover the period from 1992 to 2004. Also the forecasts of binary events (defined as the occurrence of 5, 20 or 40 mm of maximum rain), made by classification and regression ANN, were compared. Particular emphasis is given to the sounding-derived indices which are chosen in the first places by the predictor forward selection algorithm.     

Estimation of dew point temperature using neuro-fuzzy and neural network techniques

This study investigates the ability of two different artificial neural network (ANN) models, generalized regression neural networks model (GRNNM) and Kohonen self-organizing feature maps neural networks model (KSOFM), and two different adaptive neural fuzzy inference system (ANFIS) models, ANFIS model with sub-clustering identification (ANFIS-SC) and ANFIS model with grid partitioning identification (ANFIS-GP), for estimating daily dew point temperature. The climatic data that consisted of 8 years of daily records of air temperature, sunshine hours, wind speed, saturation vapor pressure, relative humidity, and dew point temperature from three weather stations, Daego, Pohang, and Ulsan, in South Korea were used in the study. The estimates of ANN and ANFIS models were compared according to the three different statistics, root mean square errors, mean absolute errors, and determination coefficient. Comparison results revealed that the ANFIS-SC, ANFIS-GP, and GRNNM models showed almost the same accuracy and they performed better than the KSOFM model. Results also indicated that the sunshine hours, wind speed, and saturation vapor pressure have little effect on dew point temperature. It was found that the dew point temperature could be successfully estimated by using T _mean and R _H variables.     

华南3 km高分辨率区域模式气温预报在贵州的质量检验

利用2018年1—10月华南3 km区域高分辨率模式08时、20时起报的气温预报和实况资料,采用线性内插法进行站点预报值处理,并从平均均方根误差及预报准确率的角度,检验分析了贵州省72 h预报内逐24 h最高(低)气温预报质量。结果表明,72 h内随着预报时效的增加,预报准确率差异较小;日最低气温预报准确率相对最高气温平均高出20%左右;08时起报的最高(低)气温预报优于20时的。同时发现,最高(低)气温的预报能力在月份上存在明显差异,6—8月预报性能总体优于其它月份;在24~48 h预报中,东北—西南向一带较贵州其它区域展现出更高的预报能力。在9个主要城市站上,最高(低)气温均表现出较高的预报技巧,其中,20时起报的兴义站24 h最低气温准确率100%。通过对2018年7月18日气温预报质量检验,最高(低)气温及35.0℃以上高温事件预报准确率均在80%左右,较好反映了天气实况。因此,华南3 km高分辨率区域模式对贵州气温预报具有较好的参考价值。     

1956—2019年昌北国际机场高影响天气变化特征

基于1956—2019年参证气象站记录的雷暴、闪电、暴雨、高温、低温、雾和霾等气候资料,利用常规气候统计及Morlet小波方法对影响昌北机场安全运营的高影响天气事件演变及周期变化规律进行统计分析。结果表明:1)雷暴多出现于春夏季,年均雷暴日数为49.8 d,呈波动下降趋势。2)春夏季闪电高发,且夏季机场附近存在较明显的闪电集中区域,闪电高频时段为13—20时,最高峰为15时。3)年均暴雨、大暴雨日数分别为5.0 d和0.8 d,呈缓慢增长趋势,暴雨集中在4—8月,大暴雨集中在4、6月,二者均在6月份最多。4)夏季高温日数呈缓慢增加趋势,7月份最多,8月份次之;冬季低温日数呈明显下降趋势,1月份最多,12月份次之。5)年均霾日数大于雾日数,霾多发于秋冬季,雾集中在冬春季,均于12月最常发生。6)冰雹、积雪、结冰、冻雨、沙尘、龙卷风等破坏性天气发生频次较小,但不应忽视此类天气的防范工作。7)暴雨、低温及高温日数均存在准2 a的周期变化。